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Article: Pillar design by combining finite element methods, neural networks and reliability: A case study of the Feng Huangshan copper mine, China

TitlePillar design by combining finite element methods, neural networks and reliability: A case study of the Feng Huangshan copper mine, China
Authors
KeywordsCase studies
Finite element method
Mine pillar design
Neural networks
Reliability
Underground mine
Issue Date2003
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/ijrmms
Citation
International Journal Of Rock Mechanics And Mining Sciences, 2003, v. 40 n. 4, p. 585-599 How to Cite?
AbstractThis paper presents a mine pillar design approach by combining finite element methods (FEMs), neural networks (NN) and reliability analysis. This practical approach is presented by examining an actual cylindrical mine pillar in a copper mine and taking into account uncertainties in ore pillar material parameters including modulus, Poisson's ratio, density and uniaxial compressive strength. The ore pillar had to be able to safely and effectively support a drilling room that occupied an open space of 3.8 m high and 55 m long and 20 m wide and at a depth of 360 m below ground surface. Three-dimensional FEM was used to simulate the mining operations and to estimate average pillar compressive stress at each operation step. A pillar performance function was established in implicit form taking into account pillar strength and pillar dimension. NN was incorporated in the FEM to substantially reduce the number of finite element calculations in establishment of the relationship between pillar compressive stress and basic random variables. Trained NN was then used to generate a database for the implicit performance function. The database was used to determine the reliability index and failure probability for each trial pillar diameter. Relationship between pillar reliability index and each of the coefficients of variation of the basic random variables was used for optimal design of pillar diameter. The optimal pillar design was used in the mining construction and functioned well. © 2003 Elsevier Science Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/70888
ISSN
2015 Impact Factor: 2.01
2015 SCImago Journal Rankings: 2.134
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorDeng, Jen_HK
dc.contributor.authorYue, ZQen_HK
dc.contributor.authorTham, LGen_HK
dc.contributor.authorZhu, HHen_HK
dc.date.accessioned2010-09-06T06:27:01Z-
dc.date.available2010-09-06T06:27:01Z-
dc.date.issued2003en_HK
dc.identifier.citationInternational Journal Of Rock Mechanics And Mining Sciences, 2003, v. 40 n. 4, p. 585-599en_HK
dc.identifier.issn1365-1609en_HK
dc.identifier.urihttp://hdl.handle.net/10722/70888-
dc.description.abstractThis paper presents a mine pillar design approach by combining finite element methods (FEMs), neural networks (NN) and reliability analysis. This practical approach is presented by examining an actual cylindrical mine pillar in a copper mine and taking into account uncertainties in ore pillar material parameters including modulus, Poisson's ratio, density and uniaxial compressive strength. The ore pillar had to be able to safely and effectively support a drilling room that occupied an open space of 3.8 m high and 55 m long and 20 m wide and at a depth of 360 m below ground surface. Three-dimensional FEM was used to simulate the mining operations and to estimate average pillar compressive stress at each operation step. A pillar performance function was established in implicit form taking into account pillar strength and pillar dimension. NN was incorporated in the FEM to substantially reduce the number of finite element calculations in establishment of the relationship between pillar compressive stress and basic random variables. Trained NN was then used to generate a database for the implicit performance function. The database was used to determine the reliability index and failure probability for each trial pillar diameter. Relationship between pillar reliability index and each of the coefficients of variation of the basic random variables was used for optimal design of pillar diameter. The optimal pillar design was used in the mining construction and functioned well. © 2003 Elsevier Science Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/ijrmmsen_HK
dc.relation.ispartofInternational Journal of Rock Mechanics and Mining Sciencesen_HK
dc.subjectCase studiesen_HK
dc.subjectFinite element methoden_HK
dc.subjectMine pillar designen_HK
dc.subjectNeural networksen_HK
dc.subjectReliabilityen_HK
dc.subjectUnderground mineen_HK
dc.titlePillar design by combining finite element methods, neural networks and reliability: A case study of the Feng Huangshan copper mine, Chinaen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1365-1609&volume=40&spage=585&epage=599&date=2003&atitle=Pillar+design+by+combining+finite+element+methods,+neural+networks+and+reliability:+a+case+study+of+the+Feng+Huangshan+copper+mine,+Chinaen_HK
dc.identifier.emailYue, ZQ:yueqzq@hkucc.hku.hken_HK
dc.identifier.emailTham, LG:hrectlg@hkucc.hku.hken_HK
dc.identifier.authorityYue, ZQ=rp00209en_HK
dc.identifier.authorityTham, LG=rp00176en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/S1365-1609(03)00042-Xen_HK
dc.identifier.scopuseid_2-s2.0-0037653675en_HK
dc.identifier.hkuros76277en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0037653675&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume40en_HK
dc.identifier.issue4en_HK
dc.identifier.spage585en_HK
dc.identifier.epage599en_HK
dc.identifier.isiWOS:000182965100011-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridDeng, J=7402612836en_HK
dc.identifier.scopusauthoridYue, ZQ=7102782735en_HK
dc.identifier.scopusauthoridTham, LG=7006213628en_HK
dc.identifier.scopusauthoridZhu, HH=7404663867en_HK

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